The present invention relates to the art of medical diagnostics. It finds particular application in conjunction with CT blood flow mapping of the brain and will be described with particular reference thereto. However, it is to be appreciated that the present invention will also find utility in conjunction with other imaging modalities, such as digital x-ray, magnetic resonance, radiation and position emission, ultrasound, and the like. The present invention is further applicable in imaging other regions of human and veterinary patients, inanimate objects, and other subjects.
In the brain, blood reaches the tissue in two modes-directly through the arteries and indirectly through other tissue. In normal, healthy brain tissue, blood reaches gray matter at 40 to 100 milliliters per 100 milliliters per minute. Gray matter tissue which receives less than 30 milliliters per 100 milliliters per minute is not adequately fed for proper functioning and may suffer irreparable damage. In white matter, cerebral blood flows are typically about one third of those for gray matter with flows under about 10 milliliters per 100 milliliters per minute being considered inadequate. The early detection of brain regions with subnormal blood flows enables corrective action to be taken before the affected tissue is irreversibly damaged.
One of the most common causes of insufficient feeding of the tissue is a blockage in the arterial blood flow. In the past, iodine has been utilized as an enhancement agent injected into the blood to facilitate the location of arterial blockages. However, brain tissue membrane blocks the iodine enhancement agent from permeating the tissue area. Because iodine is unable to pass from the blood into the tissue, iodine is only able to enhance images of blood in arteries, capillaries, and veins. Iodine is therefore unable to enhance the representations of the actual profusion of blood into the tissues.
Unlike iodine, xenon passes freely from the blood into the brain tissue. Thus, utilizing xenon gas as an enhancement agent facilitates the imaging and measurement of blood profusion into the tissue. As the concentration of xenon gas in the patient's blood rises, the concentration of xenon gas in the brain tissue also increases, asymptotically approaching equilibrium concentration. The rate of increase of gas concentration in the tissue is indicative of the blood flow rate. The equilibrium concentration, which is asymptotically approached is indicative of a partition coefficient .lambda.. The partition coefficient, which is different for different kinds of tissue, is defined as the ratio of the quantity of xenon in each unit volume or voxel of tissue to the quantity of xenon per like volume in blood. For gray matter, the partition coefficient is typically about 0.9 and for white matter is typically about 1.3. Partition coefficients which differ significantly from these values are indicative of sick or dying tissue.
The xenon concentration in the tissue of the unit volume cell or voxel at a time t is described by the formula known as the Kety equation: ##EQU1## where C is tissue xenon concentration, C.sub.a is the blood xenon concentration, K is the tissue clearance or build-up rate, and f is the flow rate. The partition coefficient .lambda. is related to the flow rate and the clearance or build-up rate by the equation: EQU f=.lambda.K (2),
where .lambda. is the tissue-blood partition coefficient.
The blood xenon concentration is readily monitorable. The tissue xenon concentration for a tissue in a given voxel can be calculated from the CT number or value of the pixel of a CT image corresponding to the given voxel. By taking several CT images at different times, with the blood xenon concentration known for times preceding each image, one can theoretically solve the Kety equation to determine the partition coefficient and blood flow for the tissue voxel corresponding to each pixel. Typically, three to six images have been taken. More particularly, the CT numbers or values from the corresponding pixels of each of the three to six images have been iteratively fit to the "best" flow f and partition coefficient which, with the "known" C.sub.a (w), allowed comparative C(t) to be calculated using any of various conventional curve fitting techniques.
Of course, C.sub.a (w) is not a directly measured value. Rather, it is approximated by making a best fit match of the data points to a single exponential curve. See for example Gur, et al., Stroke, Vol. 13, No. 6, Nov.-Dec. 1982, pp. 750-758. One major advantage of using the single exponential approximation is that it is readily able to be integrated when performing the calculation of Equation (1). Because the single exponential curve in many instances did not fit the generated data, it was proposed to use a dual-exponential. See for example Kishore, et al., JCAT, Vol. 8, No. 4, pp. 619-630 (Aug. 1984).
One of the drawbacks to the prior art exponential curve fitting techniques is that experimental data have not fit an exponential curve well for all patients. Those patients which did not match the exponential curve, often had the greatest mismatch at the latter times of data sampling. Such later samplings typically contributed more heavily to the end results. Hence, an error at the later points of the concentration curve accentuated the curve fit error. Patients with impaired lungs, such as heavy smokers, had a particularly bad fit to an exponential or double exponential curve in the latter sampled regions.
The present invention contemplates a new and improved technique for accurately determining the flow, partition coefficient, and the fit or confidence values from CT or other imaged data.